Change Detection with LiDAR Data David Streutker Naval Postgraduate School

Change Detection with LiDAR
David Streutker
Idaho State University
Boise Center Aerospace Lab
Naval Postgraduate School
LiDAR Littoral Studies Workshop
Monterey, California
May 24, 2007
Uses of change detection
LiDAR accuracy and change detection
Two methods of co-registration
Co-registration example
Change detection of a landslide
Change detection of a rangeland fire
Uses of Change Detection
• Coastal studies
– Beach erosion and/or deposition
• Hydrology
– Water levels (surface and groundwater)
– Snow pack
– Bathymetry
Aeolian transport
Tectonic and landslide movement
Vegetation monitoring
LiDAR Accuracy
• Accuracy determines amount of change
detection possible
• Absolute accuracy
– Accuracy with respect to global coordinate system
– Generally around 15 cm vertical and 50 cm
• Relative accuracy
– Accuracy within dataset (“point-to-point”)
– Can be better than 5 cm vertical
• Necessary for change detection
• “Brute force” method
– Use of least-squares to evaluate fit
– Iterative to determine best fit
– Computationally expensive
• Slope-based method
– Intelligent
• Estimates overall offset
– Flexible
• Able to use polynomial warping
– Computationally efficient
Salmon Falls Creek Landslide
Salmon Falls Creek Landslide
• Data acquired in 2002 and 2005
• 2002 data
– 1 m spacing
– High relative accuracy (< 25 cm vertical)
– NAD 27 datum
• 2005 data
– 0.5 m spacing
– Very high relative accuracy (< 10 cm vertical)
– NAD 83 datum
Accuracy of 2002 Data
• Relative accuracy “poorer” than 2005 data
• Primary reason due to small errors in flightline
– Difficulty due to rugged terrain
• Problem: Relative accuracy on the order of or
lower than the expected change
• Solution: Redo flightline co-registration
Flightline Overlap Analysis
Vertical Difference
Example: No Offset
Example: X Offset = 0.5 m
Example: X Offset = 1 m
Example: X Offset = 2.5 m
Example: X Offset = 5 m
X Offset = 1 m, Z Offset = 1 m
Vertical Offset Versus Slope
Linear relationship implies shift
Shift amount in X and Y can be determined by the slope
Offset in Z determined from flat regions
Surfaces can be corrected by shifting in X, Y, and Z
Data Density
Before and After Correction
Average Offsets
• Vertical offset
measured from
flat areas
• Horizontal offset
measured from
steep areas
• Distribution of
offsets provides
measure of
relative accuracy
Recent Landslide Activity
Components of the slide
Landslide Change Detection
• Co-registration technique applied to 2002 and
2005 datasets
– Areas of known change were masked to avoid bias
• Landslide
• Ponds and lakes
• Quarry
– Used a robust, least absolute deviation to avoid bias
from outliers
• Co-registered images were subtracted from one
another to determine change
Comparison of Profiles
Overall Vertical Shift
Vertical Difference (m)
Deconvolving Horizontal Movement
X Offset = -78 cm
Z Offset = 12 cm
U.S. Sheep Experiment Station
• Near Dubois, Idaho
• Vegetation heights of
50 - 150 cm
• Major Species
– Mountain Sagebrush,
Rabbitbrush, Horsebrush
– Thickspike wheatgrass,
Plains reedgrass, Idaho
USDA Sheep Experiment Station
• A prescribed burn took place in the fall of
• LiDAR data were acquired in the weeks
before the burn, and again soon after the
• Vegetation heights were determined from
both the pre- and post-burn data
• Surface texture products were compared to
estimate burn severity
Vegetation Roughness: 1D
Vegetation Roughness: 2D
Vegetation Roughness (cm)
Vegetation Change
• Clear burn signature
• Variations in the
amount of change
indicate burn severity
Decrease in
Roughness (cm)
Field Validation
• Burn severity
was measured
in the field
• Measurements
compare well to
change in
Sources of Error
• Accuracy of individual datasets
• Resolution of individual datasets
• Accuracy of co-registration
– Co-registration method
– Degree of warping used
– Unknown areas of change which bias the coregistration
– Number of points used
– Number of iterations
• LiDAR can be used effectively to detect and
monitor change at the sub-meter level
• LiDAR-based change detection can be used in
a variety of environments
• Statistical methods are useful for leveraging the
large amounts of data in LiDAR studies
• Care must be taken to preserve the highaccuracy of the raw LiDAR data